AB vision-fallback
Vision/image understanding for agents whose model can't read images (returns "model does not support images", empty/unknown output, low confidence, or user-reported failure). Calls an OpenAI-compatible vision API (doubao or any OpenAI-compatible provider), returns structured JSON. Use whenever an image must be understood. Do NOT substitute with local OCR (tesseract) - OCR extracts text only, not layout/visual understanding.
Vision/image understanding for agents whose model can't read images (returns "model does not support images", empty/unknown output, low confidence, or…
As a process B 68/100 · Nearly there — weak spots: result and completion, consistency
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 0. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 68/100
- 0Result and completion. Does not say what the result is
- 40Consistency. Frontmatter name (vision-fallback) differs from the folder (vision-fallback-skill)
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 10 steps, 2 vague phrases
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 2 branches, has a failure section
- 100Execution cost. Instruction body is 812 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -31 of 3 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 427: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.